An algorithmic framework for synthetic cost-aware decision making in molecular design
Name
SPARROW Manuscript.pdf
Description
Accepted version
Size
2.14 MB
Format
Adobe PDF
Checksum (MD5)
539fc74a42dd6cf85364e4cfea6503e2
Author(s)
Fromer, Jenna C
Coley, Connor W
Date Issued
June 17, 2024
Journal
Nature Computational Science
Publisher
Springer Science and Business Media LLC
Citation
Fromer, J.C., Coley, C.W. An algorithmic framework for synthetic cost-aware decision making in molecular design. Nat Comput Sci 4, 440–450 (2024).
Version
Author's final manuscript
Abstract
Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1038/s43588-024-00639-y
